SoroushVahidi/scidocs huggingface dataset:
A huge, undescribed soil ciliate (Protozoa: Ciliophora) diversity in natural forest stands of Central Europe
(2004)

We investigated 12 natural forest stands in eastern Austria for soil ciliate diversity, viz., eight beech forests and two lowland and Pinus nigra forests each. The stands span a wide range of climatic (e.g., 543–1759 mm precipitation, 160–1035 m above sea-level) and abiotic (e.g., pH 4–7.4) factors. Samples were taken twice in autumn and late spring and analysed with the non-flooded Petri dish method. Species were identified in vivo, in silver preparations, and in the scanning electron microscope. A total of 233 species were found, of which 30 were undescribed, a surprising number showing our ignorance of soil ciliate diversity, even in Central Europe. Species number varied highly from 45 (acidic beech on silicate) to 120 (floodplain forest) and was strongly correlated with pH and overall habitat quality, as measured by climate, the C/P quotient (ratio of r-selected colpodean and k-selected polyhymenophorean ciliates), and the proportion of mycophagous ciliate species; multivariate analysis showed further important variables, viz., the general nutrient status (glucose, nitrogen, C/ N ratio) and microbial (urease) activity. The highest species number occurred in one of the two floodplain soils, supporting the intermediate disturbance hypothesis. The three main forest types could be clearly distinguished by their ciliate communities, using similarity indices and multidimensional scaling. Individual numbers varied highly from 135−1 (lowland forest) to 10,925 ml−1 (beech on silicate) soil percolate and showed, interestingly, a weak correlation with soil protozoan phospholipid fatty acids. Eight of the 30 new species found and a forgotten species, Arcuospathidium coemeterii (Kahl 1943) nov. comb., are described in detail, as examples of how species were recognized and soil protozoan diversity should be analyzed: Latispathidium truncatum bimicronucleatum, Protospathidium fusioplites, Erimophrya sylvatica, E. quadrinucleata, Paragonostomum simplex, Periholosticha paucicirrata, P. sylvatica, and Australocirrus zechmeisterae.
Image Enhancement in Encrypted Domain over Cloud
(2015)

Cloud-based multimedia systems are becoming increasingly common. These systems offer not only storage facility, but also high-end computing infrastructure which can be used to process data for various analysis tasks ranging from low-level data quality enhancement to high-level activity and behavior identification operations. However, cloud data centers, being third party servers, are often prone to information leakage, raising security and privacy concerns. In this article, we present a Shamir's secret sharing based method to enhance the quality of encrypted image data over cloud. Using the proposed method we show that several image enhancement operations such as noise removal, antialiasing, edge and contrast enhancement, and dehazing can be performed in encrypted domain with near-zero loss in accuracy and minimal computation and data overhead. Moreover, the proposed method is proven to be information theoretically secure.
BLE device indoor localization based on RSS fingerprinting mapped by propagation modes
(2017)

Nowadays, Bluetooth Low Energy (BLE) technology has a great attention in the field of wireless localization techniques, especially in the case of indoor scenarios. Study presented in this paper explores BLE localization performance in indoor scenarios based on a received signal strength (RSS). Firstly, we present a Ray-Launching based application to emulate BLE radio frequency (RF) signal propagation in the office environment. Secondly, an appropriate measurement workplace and its setup is proposed to measure RSS values. Obtained results are used to create an RSS-fingerprinting map. Furthermore, accuracy of position determination is calculated and evaluated. Results show advantage of BLE technology for indoor localization purposes (without calibration measurement). However, its performance highly depends on the number of considered BLE nodes and on applied evaluation method (e.g. number of considered sectors).
Visualization of medical data based on EHR standards.
(2012)

BACKGROUND
To organize an efficient interaction between a doctor and an EHR the data has to be presented in the most convenient way. Medical data presentation methods and models must be flexible in order to cover the needs of the users with different backgrounds and requirements. Most visualization methods are doctor oriented, however, there are indications that the involvement of patients can optimize healthcare.


OBJECTIVES
The research aims at specifying the state of the art of medical data visualization. The paper analyzes a number of projects and defines requirements for a generic ISO 13606 based data visualization method. In order to do so it starts with a systematic search for studies on EHR user interfaces.


METHODS
In order to identify best practices visualization methods were evaluated according to the following criteria: limits of application, customizability, re-usability. The visualization methods were compared by using specified criteria.


RESULTS
The review showed that the analyzed projects can contribute knowledge to the development of a generic visualization method. However, none of them proposed a model that meets all the necessary criteria for a re-usable standard based visualization method. The shortcomings were mostly related to the structure of current medical concept specifications.


CONCLUSION
The analysis showed that medical data visualization methods use hardcoded GUI, which gives little flexibility. So medical data visualization has to turn from a hardcoded user interface to generic methods. This requires a great effort because current standards are not suitable for organizing the management of visualization data. This contradiction between a generic method and a flexible and user-friendly data layout has to be overcome.
“The magic finger technique” a simplified approach for more symmetric results in alar base resection
(2016)

Alar base surgery is one of the most important and challenging steps of aesthetic rhinoplasty. While an ideally shaped alar base is the goal in a desired nose, nearly all patients have asymmetric nostrils preoperatively. Ethnicity, trauma, cocaine use, or previous rhinoplasties are some factors affecting the width and shape of the nasal base. After the conclusion of all planned rhinoplasty sequences and closure of the mid-columellarincision, we mark the midline inferior to the columella at the nasolabial junction and use acaliper to measure an equal distance from the mid-columellar point to the alar creases on eachside, and mark the medial points of the alar creases. Next we draw on the natural creasesbilaterally extending to 3 o’clock on the right side and 9 o‘clock on the left side as the limit ofthe lateral excisions to avoid scarring. We then gently depress the alae and alar-facial grooveswith the index finger and allow the formation of new creases superior to the original alarcreases in order to detect excess skin to remove. After marking, the resection was performed with a no. 15 blade. The excision was closed using 6-0 Prolene sutures. We aimed to describe a simple technique for making asymmetric resections in which theapplication of pressure by a finger reveals excess skin in both nostril sill and nostril flareindependently for each alar base. With these asymmetric excisions from the right and left alar bases, a more symmetric nostrils and nasal base can be achieved. Level of Evidence: Level IV, therapeutic study.
Exploiting Eigenposteriors for Semi-Supervised Training of DNN Acoustic Models with Sequence Discrimination
(2017)

Deep neural network (DNN) acoustic models yield posterior probabilities of senone classes. Recent studies support the existence of low-dimensional subspaces underlying senone posteriors. Principal component analysis (PCA) is applied to identify eigenposteriors and perform low-dimensional projection of the training data posteriors. The resulted enhanced posteriors are applied as soft targets for training better DNN acoustic model under the student-teacher framework. The present work advances this approach by studying incorporation of sequence discriminative training. We demonstrate how to combine the gains from eigenposterior based enhancement with sequence discrimination to improve ASR using semi-supervised training. Evaluation on AMI meeting corpus yields nearly 4% absolute reduction in word error rate (WER) compared to the baseline DNN trained with cross entropy objective. In this context, eigenposterior enhancement of the soft targets is crucial to enable additive improvement using out-of-domain untranscribed data.
The five-repetition sit-to-stand test as a functional outcome measure in COPD.
(2013)

BACKGROUND
Moving from sitting to standing is a common activity of daily living. The five-repetition sit-to-stand test (5STS) is a test of lower limb function that measures the fastest time taken to stand five times from a chair with arms folded. The 5STS has been validated in healthy community-dwelling adults, but data in chronic obstructive pulmonary disease (COPD) populations are lacking.


AIMS
To determine the reliability, validity and responsiveness of the 5STS in patients with COPD.


METHODS
Test-retest and interobserver reliability of the 5STS was measured in 50 patients with COPD. To address construct validity we collected data on the 5STS, exercise capacity (incremental shuttle walk (ISW)), lower limb strength (quadriceps maximum voluntary contraction (QMVC)), health status (St George's Respiratory Questionnaire (SGRQ)) and composite mortality indices (Age Dyspnoea Obstruction index (ADO), BODE index (iBODE)). Responsiveness was determined by measuring 5STS before and after outpatient pulmonary rehabilitation (PR) in 239 patients. Minimum clinically important difference (MCID) was estimated using anchor-based methods.


RESULTS
Test-retest and interobserver intraclass correlation coefficients were 0.97 and 0.99, respectively. 5STS time correlated significantly with ISW, QMVC, SGRQ, ADO and iBODE (r=-0.59, -0.38, 0.35, 0.42 and 0.46, respectively; all p<0.001). Median (25th, 75th centiles) 5STS time decreased with PR (Pre: 14.1 (11.5, 21.3) vs Post: 12.4 (10.2, 16.3) s; p<0.001). Using different anchors, a conservative estimate for the MCID was 1.7 s.


CONCLUSIONS
The 5STS is reliable, valid and responsive in patients with COPD with an estimated MCID of 1.7 s. It is a practical functional outcome measure suitable for use in most healthcare settings.
Change Detection in Feature Space Using Local Binary Similarity Patterns
(2013)

In general, the problem of change detection is studied in color space. Most proposed methods aim at dynamically finding the best color thresholds to detect moving objects against a background model. Background models are often complex to handle noise affecting pixels. Because the pixels are considered individually, some changes cannot be detected because it involves groups of pixels and some individual pixels may have the same appearance as the background. To solve this problem, we propose to formulate the problem of background subtraction in feature space. Instead of comparing the color of pixels in the current image with colors in a background model, features in the current image are compared with features in the background model. The use of a feature at each pixel position allows accounting for change affecting groups of pixels, and at the same time adds robustness to local perturbations. With the advent of binary feature descriptors such as BRISK or FREAK, it is now possible to use features in various applications at low computational cost. We thus propose to perform background subtraction with a small binary descriptor that we named Local Binary Similarity Patterns (LBSP). We show that this descriptor outperforms color, and that a simple background subtractor using LBSP outperforms many sophisticated state of the art methods in baseline scenarios.
Customer Knowledge Contribution Behavior in Social Shopping Communities
(2013)

Social shopping communities, a special form of social media, have offered fertile ground for customers to communicate their opinions and exchange product information. Although social shopping communities have the potential to transform the way online customers acquire knowledge in everyday life, research in information systems has paid little attention to this emerging type of social media. Thus, the goal of this paper is to enhance our understanding of user behavior in this new form of community. We propose and empirically test an integrative theoretical model of customer knowledge contribution based on social capital theory. By analyzing panel data collected over two weeks from 2,251 customers in a social shopping community, we found that reputation, reciprocity, network centrality, as well as customer expertise have significant impact on customer knowledge contribution. These results contribute significantly to the literature and provide important implications for future research and practice.
A Framework for Structural Risk Minimisation
(1996)

The paper introduces a framework for studying structural risk minimisation. The model views structural risk minimisation in a PAC context. It then considers the more general case when the hierarchy of classes is chosen in response to the data. This theoretically explains the impressive performance of the maximal margin hyperplane algorithm of Vapnik. It may also provide a general technique for exploiting serendipitous simplicity in observed data to obtain better prediction accuracy from small training sets.
Analysis of H.264 Bitstream Prioritization for Dual TCP/UDP Streaming of HD video over WLANs
(2015)

Flexible Dual-TCP/UDP Streaming Protocol (FDSP) is a new method for streaming H.264-encoded HD video over wireless networks. The method takes advantage of the hierarchical structure of H.264/AVC syntax and uses TCP to transmit important syntax elements of H.264/AVC video and UDP to transmit less important elements. FDSP was shown to outperform pure-UDP streaming in visual quality and pure-TCP streaming in delay. In this work, FDSP is expanded to include a new parameter called Bitstream Prioritization (BP). The newly modified algorithm, FDSP-BP, is analyzed to measure the impact of BP on the quality of streaming for partially and fully congested networks. Our analysis shows that FDSP-BP is superior to pure-TCP streaming methods with respect to rebuffering instances, while still maintaining high visual quality.
Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics
(2017)

The term big data occurs more frequently now than ever before. A large number of fields and subjects, ranging from everyday life to traditional research fields (i.e., geography and transportation, biology and chemistry, medicine and rehabilitation), involve big data problems. The popularizing of various types of network has diversified types, issues, and solutions for big data more than ever before. In this paper, we review recent research in data types, storage models, privacy, data security, analysis methods, and applications related to network big data. Finally, we summarize the challenges and development of big data to predict current and future trends.
Big data analytics in healthcare: promise and potential
(2014)

OBJECTIVE
To describe the promise and potential of big data analytics in healthcare.


METHODS
The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions.


RESULTS
The paper provides a broad overview of big data analytics for healthcare researchers and practitioners.


CONCLUSIONS
Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however there remain challenges to overcome.
An unusual case of predation: dog pack or cougar attack?
(2013)

Injuries produced by animals are capable of leaving severe patterns and in some cases may result in the death of the attacked individual. Law enforcement authorities may come to erroneous conclusions about the source of the bites based on their awareness of animals present and similarities of the injuries to the untrained eye, with dreadful consequences. Expertise of a carnivore biologist and an odontologist that indentifies the particularities of bite marks may be useful for identifying the attacking species. We present the investigation of a fatal dog pack attack involving a 43-year-old man in Bell Ville (Argentina) where the evidence provided by a forensic dentist and a biologist was categorical for establishing the animal species involved. Because of the unusual characteristics of the wounds and the initial hypothesis made by local authorities of a cougar attack, habits and specific patterns of both dog pack and cougar predation on humans are discussed.
Diagnosability of discrete-event systems
(1995)

Abstruct-Fault detection and isolation is a crucial and challenging task in the automatic control of large complex systems. We propose a discrete-event system (DES) approach to the problem of failure diagnosis. We introduce two related notions of diagnosability of DES’s in the framework of formal languages and compare diagnosability with the related notions of observability and invertibility. We present a systematic procedure for detection and isolation of failure events using diagnosers and provide necessary and sufficient conditions for a language to be diagnosable. The diagnoser performs diagnostics using online observations of the system behavior; it is also used to state and verify off-line the necessary and sufficient conditions for diagnosability. These conditions are stated on the diagnoser or variations thereof. The approach to failure diagnosis presented in this paper is applicable to systems that fall naturally in the class of DES’s; moreover, for the purpose of diagnosis, most continuous variable dynamic systems can be viewed as DES’s at a higher level of abstraction. In a companion paper [20], we provide a methodology for building DES models for the purpose of failure diagnosis and present applications of the theory developed in this paper.
A NOVEL OF RECONFIGURABLE PLANAR ANTENNA ARRAY ( RPAA ) WITH BEAM STEERING CONTROL
(2010)

A new antenna structure is formed by combining the concept of reconfigurable planar antenna array (RPAA) with the parasitic elements to produce beam steering patterns. The antenna has been integrated with the PIN diode switches that enable the beam to be steered in the desired direction. This has been done by changing the switch state to either on or off mode. In this work, a number of parasitic elements have been applied to the antenna, namely reflectors and directors. They are placed in between the driven elements, which is aimed to improve the beam steering angle. With such configuration, the main beam radiated by the array can be tilted due to the effect of mutual coupling between the driven elements and parasitic elements (reflectors and director). The unique property of this antenna design is that instead of fabricating all together in the same plane, the antenna’s feeding network is separated from the antenna radiating elements (the patches) by an air gap distance. This allows reducing the spurious effects from the feeding line. The optimization results for the resonant Corresponding author: M. T. Ali (mizi732002@yahoo.com).
A Deep Learning Approach to Universal Skin Disease Classification
(2015)

Skin diseases are very common in people’s daily life. Each year, millions of people in American are affected by all kinds of skin disorders. Diagnosis of skin diseases sometimes requires a high-level of expertise due to the variety of their visual aspects. As human judgment are often subjective and hardly reproducible, to achieve a more objective and reliable diagnosis, a computer aided diagnostic system should be considered. In this paper, we investigate the feasibility of constructing a universal skin disease diagnosis system using deep convolutional neural network (CNN). We train the CNN architecture using the 23,000 skin disease images from the Dermnet dataset and test its performance with both the Dermnet and OLE, another skin disease dataset, images. Our system can achieve as high as 73.1% Top-1 accuracy and 91.0% Top-5 accuracy when testing on the Dermnet dataset. For the test on the OLE dataset, Top-1 and Top-5 accuracies are 31.1% and 69.5%. We show that these accuracies can be further improved if more training images are used.
Network Hardware-Accelerated Consensus
(2016)

Consensus protocols are the foundation for building many fault-tolerant distributed systems and services. This paper posits that there are significant performance benefits to be gained by offering consensus as a network service (CAANS). CAANS leverages recent advances in commodity networking hardware design and programmability to implement consensus protocol logic in network devices. CAANS provides a complete Paxos protocol, is a dropin replacement for software-based implementations of Paxos, makes no restrictions on network topologies, and is implemented in a higher-level, data-plane programming language, allowing for portability across a range of target devices. At the same time, CAANS significantly increases throughput and reduces latency for consensus operations. Consensus logic executing in hardware can transmit consensus messages at line speed, with latency only slightly higher than simply forwarding packets. Report Info
Major components of the gravity recommendation system
(2007)

The Netflix Prize is a collaborative filtering problem. This subfield of machine learning became popular in the late 1990s with the spread of online services that used recommendation systems (e.g. Amazon, Yahoo! Music, and of course Netflix). The aim of such a system is to predict what items a user might like based on his/her and other users' previous ratings. The Netflix Prize dataset is much larger than former benchmark datasets, therefore the scalability of the algorithms is a must. This paper describes the major components of our blending based solution, called the Gravity Recommendation System (GRS). In the Netflix Prize contest, it attained RMSE 0.8743 as of November 2007. We now compare the effectiveness of some selected individual and combined approaches on a particular subset of the Prize dataset, and discuss their important features and drawbacks.